System and parametric method for cancer discriminations

ABSTRACT

There is provided a system and method for detection of cancerous cells from an organ tissue. The system includes a plurality of cells obtained from the organ tissue, a light source for directing light through the plurality of cells and an image sensor for detecting and measuring optical transmission characteristics of the plurality of cells. The proposed method includes modeling the measured optical transmission characteristics using a statistical algorithm and calculating a figure of merit (FOM) for each of the plurality of cells for enhancing identification accuracy of cancerous cells, wherein the FOM is calculated from pole coefficients and locations corresponding to the plurality of cells.

FIELD OF THE INVENTION

The present invention relates to a method and system for cancer celldiscrimination, and more particularly a method combining opticalmeasurements and statistical techniques for early cancer detection.

BACKGROUND OF THE INVENTION

Background description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

It is crucial for diagnosing diseases such as cancer at an early stage.Although the symptoms of cancer are not apparent at the initial stage,if cancer spreads, effective treatment is a grueling task and,generally, the patient's survival rate is very low. When diagnosed at anadvanced stage, more than 90% of women with breast cancer have beenfound to survive the disease for 10 more years compared to less than 20%of women surviving for 5 years. Approximately 93% of patients diagnosedwith colon cancer and detected at an early stage have 5-year survivalrates compared to those diagnosed at a later stage. A similar increasein survival rate is found for other types of cancer when detected early.Accordingly, in the past two decades, extensive research has beencarried out to develop various means that aid in the efficientclassification of normal and cancer cells. The classification of normaland cancer cells is very important because it assists in early detectionof cancer and thereby helps cancer patients to receive better treatmentand improve their chances of survival.

Considering traditionally employed methods, Mukhopadhyay et al. used theEmpirical Mode Decomposition (EMD) technique for detecting cancer at anearly stage. The signals obtained from elastic scattering spectroscopyfrom the normal and cancer cells are processed using the EMD technique.The optical signal response is decomposed into a set of finite numbersof band limited signals known as the intrinsic mode function (IMF). Thearea parameter of each IMF obtained for normal and cancer cervicaltissues is used as a tool for discriminating the tissues. The resultsshow that the algorithm is efficient in sorting normal and canceroustissues. However, EMD has limitations in discriminating the componentsin narrowband signals. Li et al. applied a genetic algorithm (GA)combined with linear discriminant analysis (LDA) as a signal processingtechnique in the detection of nasopharyngeal cancer. The spectraobtained from surface-enhanced Raman Spectroscopy for malignant andbenign tissues are analyzed using the GA-LDA method. The alterations inthe features of the Raman spectra of normal and cancer tissues are usedfor differentiating the tissues. The GA-LDA algorithm is utilized tolook for the dominant features of the spectra. The algorithm, althoughit worked efficiently for cancer tissue discrimination based on featureselection, has limitations. The algorithm has to be run more than 100times to select the appropriate spectral bands. The overall accuracy ofthe diagnostic model is 76.9%. Li et al. used the same (GA-LDA)technique for detecting bladder cancer. As mentioned earlier, thedownside of this technique is that the algorithm is executed more than100 times and Raman variables are searched for characterizing bladdercancer in each run.

Duraipandian et al. reported the use of GA along with partial leastsquares-discriminant analysis (PLS-DA) with double cross-validation(dCV) for the feature selection from Raman spectra of normal andcancerous cervical tissues. The results show a diagnostic accuracy of83% in discriminating cancerous and normal cervical tissues.Franceschini et al. applied a projection image transformation algorithmfor processing images of breast tissues. The optical images processed bythis technique enhance the features that show the inhomogeneity innormal and cancerous tissues. The spatial resolution of the opticalmethod used in this work is 1 cm but they can detect tumors of smallersize if the images have good optical contrast. Salomatina et al. haveinvestigated the optical differences between cancerous and normal skincells. They have utilized a sphere spectrophotometer to conduct theabsorption and transmittance measurements. Optical properties such asabsorption and scattering coefficients of the normal and cancer skin areobtained from the measured quantities using a quasi-Newton inversealgorithm and the Monte Carlo technique. The efficiency of thequasi-Newton inverse algorithm is that it requires many fewer iterations(less than 10) to reach convergence. The optical parameters obtained arestatistically acceptable if the probability value is less than 0.05,which means the optical properties of the normal and cancer tissuesdiffer by more than 95%.

The Prony technique, introduced in 1795 by Gaspard de Prony to describegas expansion, is a well-known signal analysis technique. It has wideapplication in signal processing in the biomedical field. In biomedicalsignal processing, the Prony technique is prominently used for thecharacterization of tumors, for cancer detection and for power spectrumestimation of DNA sequences. Hauer employed the Prony method fordetermining the modal components of the signal response obtained from aWestern U.S. power system. The signal components extracted using theProny technique—in combination with Fourier techniques and frequencydomain approaches—are used for dynamic modeling of the power system. Theresults show that the Prony algorithm gave a good fit with a reasonableSNR value for the high noise signal. The Prony method is used forfinding low frequency oscillations in power systems. Xiao et al.compared the Fourier Transformation (FFT) technique and the Pronytechnique in their study identifying low frequency oscillations in powersystems and concluded that Prony is a competent technique compared toFFT. The simulation results show that the technique is efficient foridentifying low frequency oscillations in real grids.

Chuang et al. applied the Prony analysis technique on a synthesizedsignal that represents the backscattered signal from radar targets.Further, the Prony algorithm is used to deduce natural resonances of thetargets. The resonances obtained using the Prony method are used fortarget detection and discrimination. It was concluded that the resultsobtained through the Prony method in the absence of noise are morereliable than those from the numerical search procedure. The lengthycomputation time in numerical search methods is greatly overcome byusing Prony's method. Marple et al. discussed the use of Prony's methodto detect and classify acoustic transient signals obtained fromsubaquatic sonar sensors. The energy component coupled to the poleamplitude and damping constants of the estimated model is used as a keyfor transient detection and for extraction of features used inclassification. The results show that the technique worked very welleven in the presence of noise in the signal.

Furthermore, the Prony algorithm is widely used in biomedical signalprocessing for tumor detection. Huo et al., in an attempt to modelbreast tumors, reported the use of the Prony method. The tumor in thebreast is represented as a concealed dielectric target. When it issubjected to a short EM pulse, it backscatters a signal that includescomplex natural resonances (CNR), which is equivalent to the poles ofthe tumor. The Prony method gives the poles and residues from the timedomain backscattered signal. The complex natural resonance can becorrelated with the morphological and intrinsic composition of thetumor. Hence, the optical and electrical properties can be used todetect and identify tumors. Li et al. utilized an approach similar tofor characterizing breast tumors based on 2D-FDTD simulation. Thetime-domain response of the tumors is obtained through FDTD simulationand is analyzed using the Prony technique for characterizing the tumors.The results are promising in characterizing breast tumors when used incombination with imaging diagnosing methods such as ultrasound imaging,confocal microwave imaging and so on.

Wang et al. discussed the use of the Prony technique for extraction ofpoles from noisy data for tumor characterization. The results show thatthe poles extracted using the Prony technique gave accurate results evenwhen the detected signal was mixed with a limited level of noise. Banniset al. employed the Prony technique for breast cancer tumor detectionfrom a scattered field electromagnetic (EM) signal. The poles extractedfrom the scattered EM signal are used as a tool for breast cancerdetection. In another work to study the effect of the chest wall onbreast tumor detection, the Prony algorithm was utilized for polesextraction. Gale et al. utilized the Prony method for estimatingparameters of a nuclear magnetic resonance (NMR) signal obtained fromblood plasma for the early detection of cancer. Roy et al. suggested amethod to estimate the power spectral density of a DNA sequence. In thisapproach, the simulation results from Prony's all-pole model efficientlydistinguished the coding and noncoding regions of a DNA sequence.

Conventional cancer screening techniques based on clinical studies aremostly invasive. Furthermore, these techniques require large amounts ofsamples, biomarkers, antigens, and antibodies. Biomarkers are moleculesthat are present in blood, urine, stool, tissues, or other bodily fluidsthat indicate normal or abnormal processes in the body. Cancerbiomarkers include substances produced by the cancer cells or by othercells reacting to the cancer cells in the body. These biomarkers arehelpful in detecting and diagnosing cancer cells. However, an importantdownside of these techniques is that repeated biopsies are required ifthe false positive rate is high. As a result, the emphasis on thedevelopment of reliable, label-free methods is growing inside theresearch community.

A non-invasive discrimination method of cancer cells from normal cellsin a corresponding adherent culture has been introduced utilizing theircell morphology. The analysis of the corresponding intracellulardistribution phase-shift data has been used to develop cancer index asan indicator to be utilized to distinguish the cancer from normal cells.On the other hand, cancer and normal cells were discriminated based ontheir biomechanics characteristics. The cellular properties were probedat the single cell level using atomic force microscopy (AFM). Thecorresponding elastic properties were measured with a proper designedindentation experiments. The extracted Young's modulus that representsthe cellular deformability can be used quantitatively to distinguish thenormal form cancer. Chromatography-mass spectrometry based on gas orbiomarkers has been used for the metabolomics profiling of cells. Withthe help of multivariate analysis, a panel of metabolites revealed thatwas possible to discriminate cancerous from normal cells.

Electrically, a set of extracted parameters from the measuredcapacitance-voltage profiles has been used to recognize cancer fromnormal cells. The normal cells exhibit higher dielectric constantscompared to cancer cells from the same tissue. On another study theelectrical impedance spectroscopy has been employed to classify betweennormal and cancerous mammalian cells. Set of features allowed theclassification of the samples in normal or cancerous with 4.5% of falsepositives and no false negatives. The interactions between cellcompositions and light were used to discriminate and identify severaltypes of cancer and normal cells. Empirically, the cancer cells exhibithigher transmittance intensity when compared to normal ones from thesame tissue type. Artificial intelligence and deep learning techniquesand method have proven to show an outstanding performance andcapabilities in resolving recognition and classification problems. Acombined deep learning approach in conjunction with expert trained datasystem was very powerful tool to identify cancer from gene expressiondata. Moreover, it can also contribute towards the understanding thecomplex nature of cancer based on large public data as well.

Accordingly, there exists a need for a system or method for earlydetection of cancerous cells from an organ tissue, which overcomes thedrawbacks of the above elaborated traditional methods.

SUMMARY OF THE INVENTION

Therefore it is an object of the present invention to propose a methodand system for cancer cell discrimination, and more particularly amethod combining optical measurements and statistical techniques forearly cancer detection.

The invention discloses a label-free method of detection of cancerouscells from an organ tissue, the method including the steps of extractinga plurality of cells from the organ tissue, observing and measuringoptical transmission characteristics of the plurality of cells using aspectrophotometer, modeling the measured optical transmissioncharacteristics using a statistical algorithm and calculating a figureof merit (FOM) for the plurality of cells, wherein the FOM is calculatedfrom pole coefficients and locations corresponding to the plurality ofcells.

In an embodiment of the present invention, the method further includesextracting a plurality of data parameters from the modelled opticaltransmission characteristics and processing the extracted dataparameters to obtain the pole coefficients and locations correspondingto the plurality of cells.

In another embodiment of the present invention, the plurality of cellsextracted from the organ tissue are suspended in a cell culture medium.

In another embodiment of the present invention, the measured opticaltransmission characteristics comprise reflection, absorption andvariable attenuation parameters.

In another embodiment of the present invention, modeling of the measuredoptical transmission characteristics comprises fitting the measuredoptical transmission characteristics with a sum of decaying exponentialsignals using Prony algorithm.

In another embodiment of the present invention, the measured opticaltransmission characteristics are reconstructed using the Prony algorithmwith a pre-defined fitting order.

In another embodiment of the present invention, an Autoregressive (AR)modeling technique is used to model the measured optical transmissioncharacteristics of the plurality of cells.

In another embodiment of the present invention, analysis of variance(ANOVA) statistical approach is incorporated for determining significantAR coefficients, and poles are extracted from the determined significantAR coefficients, to provide a demarcation for cancerous cells.

In another embodiment of the present invention, variations in opticaltransmission characteristics of the plurality of cells are observed, dueto differences in cell composition and intrinsic characteristics betweennormal and cancerous cells.

In another embodiment of the present invention, different coefficientsand locations are observed for the plurality of cells, due todifferences in cell composition and intrinsic characteristics betweennormal and cancerous cells.

In another embodiment of the present invention, the FOM is defined asFOM (p)=C(p)/L(p), where C(p) and L(p) represent the pole coefficientsand locations respectively, for the suspended plurality of cells.

In another embodiment of the present invention, pole quality (Q) is usedas a figure of merit (FOM).

As another aspect of the present invention, a system for detection ofcancerous cells from an organ tissue is disclosed, including a pluralityof cells extracted from the organ tissue, a light source for directinglight through the plurality of cells, an image sensor for detecting andmeasuring optical transmission characteristics of the plurality ofcells, modeling the measured optical transmission characteristics usinga statistical algorithm and calculating a figure of merit (FOM) for eachof the plurality of cells, wherein the FOM is calculated from polecoefficients and locations corresponding to the plurality of cells.

In another embodiment of the present invention, the plurality of cellsextracted from the organ tissue are suspended in a cell culture medium.

In another embodiment of the present invention, the image sensor detectsand converts absorption, variable attenuation and reflectance, caused bythe light source passing through the plurality of cells into electricalsignals.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter that is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other aspects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIGS. 1A and B depict measured optical transmittance response of HeLacells and 293T cells fitted with Prony estimations, in accordance withthe present invention.

FIG. 2 depicts extracted parameters versus number of exponentials in thefitted model for 293T cell line. FIG. 2A depicts amplitude, FIG. 2Bdepicts damping factor, FIG. 2C depicts frequency and FIG. 2D depictsphase, in accordance with the present invention.

FIG. 3A depicts Z-plane plot (unit circle) showing coefficients (inblue) and locations of poles (in red) for HeLa cell suspensions, inaccordance with the present invention.

FIG. 3B depicts Z-plane plot (unit circle) showing coefficients (inblue) and locations of poles (in red) for 293T cell suspensions, inaccordance with the present invention.

FIG. 4 depicts an extracted figure of merit (FOM) for 293T and HeLa, inaccordance with the present invention.

FIGS. 5A-5D depicts the extracted figure of merit (FOM) for normal andcancer cell lines from same tissue. FIG. 5A shows the FOM for lungnormal, FIG. 5B shows the FOM for lung cancer, FIG. 5C shows FOM forliver normal and FIG. 5D shows the FOM for liver cancer, in accordancewith the present invention.

FIG. 6A depicts figure of merit distributions for lung normal and cancercells in accordance with the present invention.

FIG. 6B depicts figure of merit distributions for liver normal andcancer cells, in accordance with the present invention.

FIG. 7 is a schematic for the experimental setup, in accordance with thepresent invention.

FIG. 8A shows measured optical transmittance response of a cancerousliver, FIG. 8B shows measured optical transmittance response of a normalliver, FIG. 8C shows measured optical transmittance response of acancerous lung and FIG. 8D shows measured optical transmittance responseof normal lung cells in accordance with the present invention.

FIG. 9A depicts the distribution of poles of the normal and cancer cellsof the lung and FIG. 9B depicts the distribution of poles of the normaland cancer cells of the liver in the z-plane.

FIG. 10A shows the Z-plane showing distribution of the reduced poles ofnormal and cancerous lung cells and FIG. 10B shows the Z-plane showingdistribution of normal and cancerous liver cells, in accordance with thepresent invention.

FIG. 11A depicts the Z-plane showing distribution of the Q-factor ofnormal and cancerous lung cells and FIG. 11B depicts the Z-plane showingdistribution of the Q-factor of normal and cancerous liver cells, inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The aspects of a method and system for the discrimination of normal andcancer cells at an early stage, according to the present invention willbe described in conjunction with FIGS. 1-11. In the DetailedDescription, reference is made to the accompanying figures, which form apart hereof, and in which is shown by way of illustration specificembodiments in which the invention may be practiced. It is to beunderstood that other embodiments may be utilized and logical changesmay be made without departing from the scope of the present invention.The following detailed description, therefore, is not to be taken in alimiting sense, and the scope of the present invention is defined by theappended claims.

The main objective of the proposed invention is for the discriminationof normal and cancer cells at an early stage. Various methods andtechniques, such as the empirical mode decomposition technique, agenetic algorithm, a projection image transformation algorithm, aquasi-Newton inverse algorithm, and the Prony technique have beenutilized for distinguishing normal and cancer tissues or cells.Label-free methods based on electrical, mechanical, optical, andbiochemical cancer cell detection techniques have been reported in theresearch literature. The analysis of electrical, mechanical, and opticalresponses of cells combined with numerical methods is gaining popularitydue to their improved efficiency in distinguishing between normal andcancer cells. Several signal processing algorithms such as prony, thematrix pencil method, and empirical mode decomposition, as well asmodeling techniques such as autoregressive (AR), autoregressive movingaverage (ARMA), and autoregressive integrated moving average (ARIMA) areused to classify normal and cancer cells.

A preferred embodiment of the present invention combines advancement inoptical measurements and Prony techniques to enhance the label-freebased classification of cells based on their measured optical profiles.In an embodiment, six kinds of cells, HeLa, 293T, lung—cancer andnormal, and liver—cancer and normal, are suspended in a cell culturemedium or their corresponding media and respective transmissioncharacteristics are collected. The cell lines under investigations—HeLa,293T, lung and liver cells are taken from different tissue organs.However, the lung (as well as liver) healthy and cancer cell lines aretaken from the same organ tissue. The transmission profiles are thenfitted with a sum of decaying exponential signals using the Pronyalgorithm. A figure of merit is introduced, whose distribution in thecomplex z-plane plays a major role in the classification of cell type.The alteration in the values of FOM is due to the changes in cellcomposition and intrinsic characteristics of different cells.

The cell lines used in the present invention are procured per theAmerican Tissue Culture Collection (ATCC) standard. Each type of cell iscultured in a medium that is specific for the cell type. Based on thetype and feature of cells, the nutritional requirements for its growthin vitro also differ. This difference in nutritional requirements isapplicable for normal and cancerous cells of the same tissue. A summaryof the cells used in the present invention is shown in Table 1.

TABLE 1 CELL MODEL TISSUE CELL TYPE 1 BEAS 2B Lung Normal 2 HCC-827 LungCancerous 3 THLE2 Liver Normal 4 HEP G2 Liver Cancerous 5 HEK 293TKidney Normal 6 HeLa Cervical Cancerous

In an embodiment, a humidified air ambience with 5% carbon dioxide (CO₂)at 37° C. is maintained for all the cells. Considering BEAS 2B—normallung cells, as per the ATCC guidelines, the culture plates on which thecells were cultured are precoated with a precoating mixture. The mixtureused for BEAS 2B cells contains fibronectin (0.01 mg/mL), bovinecollagen (0.03 mg/mL) and bovine serum albumin (0.01 mg/mL) diluted inbronchial epithelial basal medium (BEBM). Supplements such as penicillin(100 units/mL) and streptomycin (100 mg/mL) are added to the medium. Fortrypsinization, an EDTA solution (0.53 mM) with 0.5%polyvinylpyrrolidone (PVP) is used. Considering HCC-827—lung cancercells, the ATCC-recommended medium suitable for culturing CC-827 lungcancer cells is the Roswell Park Memorial Institute (RPMI) 1640 medium.The medium is suitable for culturing a variety of mammalian leukemiccells. The medium possesses a 10% heat-inactivated fetal bovine serum(FBS) supplement as base. The trypsinization of the cells is done with0.25% trypsin (a 0.53 mM EDTA solution).

Considering THLE2—normal liver cells, a mixture consisting of 2.9 mg/mLof collagen I, 1 mg/mL of fibronectin, and 1 mg/mL of bovine serumalbumin in BEBM is used as a precoating mixture coated on the culturingplates. The supplements for the media are heat-inactivated FBS(HyClone™, US—10%) and penicillin-streptomycin (Gibco—1%).Trypsinization is carried out with 0.5% trypsin (0.53 mM EDTA solution).Considering HEPG2—liver cancer cells, the HEPG2 cancer cells from livertissue are grown in Dulbecco's modified Eagle's medium (DMEM—HyClone™)in culture plates. Ten percent of FBS (HyClone™, US) and 1% ofpenicillin-streptomycin (Gibco) are supplements for the medium. Per ATCCguidelines, trypsinization for these cells is done using 0.5% trypsin(0.53 mM EDTA solution). Considering 293T—normal kidney cells, thesenormal cells from kidney tissue are cultured in DMEM (HyClone™) base.The medium is supplemented with 10% FBS and antibiotics such aspenicillin-streptomycin and gentamicin. Considering HeLa—cervical cancercells, according to the ATCC standard, the HeLa cells are cultured inDMEM (HyClone™) with 7% fetal calf serum (FCS) and the antibioticsPenStrep and gentamicin as supplements; this cell line best displays therelationship between the input-output signals. The cells are subcultured and trypsinized as per the ATCC protocol. Each type of cell issuspended and cultured separately.

The transmission profile of the cells is measured using a JASCO (V-670)spectrophotometer. A light beam from a xenon light source is split intoits component monochromatic beams by diffraction grating. The singlewavelength beam is divided into two equal-intensity beams. One of thetwo beams is the reference beam that passes through a cuvette loadedwith only the media. The second beam passes through a transparentcontainer loaded with cells in the media. The container is a highprecision cell made of quartz superasil with light path of 1 mm and anarea of 2 by 2 mm². The spectrometer has an electronic detector thatmeasures the intensities of the light beam. Based on the measuredintensities, the transmittance of the cells is determined. Consideringthe sensor and light source, Mini-Spectrometers C11708MA are employed,and the optical sensor used, converts variable attenuation orreflectance into signals.

In accordance with the present invention, the measured optical responsesare fitted or modeled with a sum of damped exponential signals as givenin equation (1):

$\begin{matrix}{{{{y\lbrack n\rbrack} = {\sum\limits_{i = 1}^{p}{A_{i}{e^{j\;\theta_{i}} \cdot e^{{({\alpha_{i} + {j\; 2\;\pi\; f_{i}}})}{T_{s}{({n - 1})}}}}}}};{n = 1}},{2\mspace{14mu}\ldots\mspace{14mu} N}} & (1)\end{matrix}$where N is the number of samples, and p is the order of the fittedmodel, which is same as the total number of damped exponentialcomponents in the summation. The least number of exponentials that givesthe best fitting is considered the optimum order of the fitted model.The complexity of the fitted model increases with the increase in theorder number. The exponential component has amplitude A_(i) (same unitas y[n]), frequency f, (Hz), damping factor α, (per second), and initialphase θ_(i) (in radian). T_(s) is the sampling interval betweenconsecutive data samples. Using Z-transformation, equation (1) isexpressed as follows:

$\begin{matrix}{{y\lbrack n\rbrack} = {\sum\limits_{i = 1}^{p}{h_{i}z_{i}^{n - 1}}}} & (2)\end{matrix}$

where h_(i) represents the coefficient (magnitude) of the estimatedpoles and z_(i) denotes the location of the poles. These parameters areexpressed as:h _(i) =A _(i) e ^(jθ) ^(i)   (3)z _(i) =e ^((α) ^(i) ^(+j2πf) ^(i) ^()T) ^(s)   (4)

The sampled data is preprocessed prior to fitting and parameterextraction. The first step in preprocessing is to eliminate noise fromthe data. This is done by smoothing the data. Data smoothing is followedby data detrending to remove trends, if any, from the measured sequence.The detrending operation gives a more accurate linear model that bestdescribes the relationship between the input-output signals. Based onthe observation of the pole coefficients and locations, a figure ofmerit (FOM) is introduced for the discrimination between normal andcancer cells from the same tissue.

In an embodiment of the present invention, six types of cells areutilized. In addition to these cell lines, the HeLa and 293T cells areutilized to carry out the proposed current approach in terms ofdetection capabilities. Normal and cancer cells of lung and liver areboth used to demonstrate cell identification. The normal and cancercells are taken from same tissues. Using a hemocytometer, the cellconcentration in each suspension is adjusted to 10′ cells per mL with 5%mean error. Subsequently, each type of cell suspension is loaded in theexperimental setup and the optical transmission of the cells is measuredover the wavelength of 590-1093 nm with a spectral resolution of 20 nm.The recorded transmittance of the responses, after de-embedding themedia and holder contributions, are sampled with a step of 2.3 nm. Thede-embedding of the media and holder contributions are then performed bysubtracting the suspension responses directly from the filled controlmedial response.

FIGS. 1A and B of the present invention show signal intensities varyingwith wavelength (i.e., measured optical transmittance response of HeLacells and 293T fitted with Prony estimations. The measured transmittanceis sampled at a uniform sampling interval of Ts=2.3 nm. This results ina number of samples of N=256). As the measured signal exhibits transientbehavior, a wavelength modified Prony algorithm can be applied. FIGS. 2Aand B depict the measured optical response superimposed with the Pronyestimated signal for the HeLa and 293T cell lines, respectively. Theleast number of exponentials that gives the best fitted model isconsidered the optimum order of the model. The optimum order (p) is 40,which is the minimum required order that provides the excellent fitting.A higher order (higher p values) result in redundancy and requirefurther processing resources. It is recommended to apply the same orderto both the 293T and HeLa cell suspensions for fair comparison.Parameters such as amplitude, frequency, phase and damping factor of theexponentials are extracted from the fitted response of each type ofcell.

FIG. 2A to 2D graphically show the plots of amplitude, damping factor,frequency and phase, respectively, obtained for 293T with a fittingorder of 40. FIG. 2A depicts amplitude, FIG. 2B depicts damping factor,FIG. 2C depicts frequency and FIG. 2D depicts phase, in accordance withthe present invention. The measured data are smoothed using theSavitzky-Golay method. This is used to increase the data precisionwithout distorting the signal tendency. The extracted parameters arefurther processed to extract the corresponding coefficients and polelocations. The coefficients and locations of poles are computed usingequations (3) and (4). The extracted coefficients and location of polesfor the HeLa and 293T cell suspension are illustrated in FIGS. 3A and3B, respectively. FIG. 3A depicts Z-plane plot (unit circle) showingcoefficients (in blue) and locations of poles (in red) for HeLa cellsuspensions, in accordance with the present invention. FIG. 3B depictsZ-plane plot (unit circle) showing coefficients (in blue) and locationsof poles (in red) for 293T cell suspensions, in accordance with thepresent invention. A review of Prony's method regarding the signalapproximation is conducted using MATLAB code. Rodriguez's algorithms andcodes are adopted in the present invention.

As illustrated in FIGS. 3A and B, the extracted poles are located withinthe unit circle of the z-plane. The y-axis represents the imaginary partand the x-axis represents the real part. The coefficients of 293T arefocused around the origin point when compared to HeLa in the z-plane.Considering that the distribution of the coefficients and poleslocations is not very helpful to be used for cell identification, afigure of merit (FOM) is introduced for better identification accuracy.The FOM is defined as follows:FOM(p)=C(p)/L(p)  (5)where L(p) and C(p) represent the location and coefficients of thepoles, respectively. The computed FOM is then normalized for each typeof cell with its corresponding maximum value. FIG. 4 illustrates the FOMfor both HeLa and 293T cell lines. The FOM for the HeLa distribution isvery close to the center of the unit circle compared to the 293Tdistribution. Although the Prony algorithm was developed for modelingsignals in the time domain, it can be applied for responses obtained infrequency domains as well (in conventional methods, the poles have beenextracted directly from the frequency response using a technique that isanalogous to Prony).

FIG. 4 shows the extracted FOM for HeLa and 293T cells. The FOM for theHeLa distribution is very close to the center of the unit circle.Significant differences in cell composition for normal and cancer cellshave been reported. Their interaction with light cause a change in theoptical absorption and transmission response. Due to differences in thecomposition of the different type of cells, the light interaction withthe cells causes an alteration in their optical and transmissionresponse. The modifications of the optical response from normal tocancer are explained mainly by morphological changes, modification ofits physiological and biochemical properties that affect the refractiveindex and allow them to be differentiated from each other. The polelocations and coefficients are affected accordingly. As testedexperimentally, cancer cells exhibit higher transmittance intensity whencompared to normal ones from the same tissue type. The FOM isproportional inversely with L(p) and therefore, for corresponding highcoefficients, lower FOM values are obtained. Further, the complex polesare defined as σ±jω, where σ is the damping coefficient and ω is theresonant pulsation. The damping and resonant poles are smaller in cancercells compared to normal cells. Therefore, the FOM becomes smaller forcancer cells than for normal cells.

Based on these results, it is shown that coefficients and pole locationsvary with composition and cell morphology and the main differencebetween normal and cancer cells of the same tissue is in terms ofcomposition and morphology. Hence, the proposed FOM is a distinctiveparameter that can be used to explore the detection and identificationof normal and cancer cells. This is possible when the technique is usedonly for fitting the response in the frequency domain to the sum of thedamped exponential and for parameter extraction. The objective here isto make inferences from the obtained parameters and for furtherprocessing. The present invention demonstrates the validity of using theProny technique to model a frequency domain signal, as the extractedparameters are used for making inferences for cell identification—andthereby making it possible to classify normal and cancerous cells forthe same tissue.

Therefore, the FOM for lung and liver normal and cancerous suspensionsare extracted per the introduced procedure and are depicted in FIG. 5.FIG. 5 depicts the extracted figure of merit (FOM) for normal and cancercell lines from same tissue. FIG. 5A shows the FOM for lung normal, FIG.5B shows the FOM for lung cancer, FIG. 5C shows FOM for liver normal andFIG. 5D shows the FOM for liver cancer, in accordance with the presentinvention. The distribution of the FOM is closer to the origin whencompared with the normal distribution. Each plotted measurementrepresents the average of 15 measurements. The multiple measurements areconducted on different aliquots taken from the same sample suspension inthe same region spot. The error bars in the subfigures of FIG. 5represent the average values along with maximum and minimum values. Thebar corresponding to the x-axis represents the average in the FOM realpart, while the endpoints represent its maximum and minimum values. Thebar corresponding to the y-axis represents the average in the FOMimaginary part, while the endpoints represent its maximum and minimumvalues. For further investigations, the distribution of the FOM fornormal and cancer cells are superimposed on each other, as depicted inFIG. 6.

FIG. 6A depicts figure of merit distributions for lung normal and cancercells in accordance with the present invention. FIG. 6B depicts figureof merit distributions for liver normal and cancer cells, in accordancewith the present invention. FIG. 6A superimposes the FOM of the lungnormal and cancer corresponding FOMs. FIG. 6B superimposes the FOMs forthe liver normal and cancer cell lines. A majority of the real part ofthe cancer corresponding poles is located in the right hand (side) ofthe plot, and a majority of the real part of the normal correspondingpoles are located in the left hand (side) of the plot.

The figure of merit (FOM) proposed in the present invention correlatesthe location of the poles (L(p)) and C(p)) the locations and coefficientof poles. Scientifically, significant differences in cell compositionfor normal and cancer cells have been reported, and hence, theirinteraction with light will cause a change in the optical absorption andtransmission response. Due to differences in the composition of thedifferent type of cells, the light interaction with the cells causes analteration in their optical and transmission response. The modificationsof the optical response from normal to cancer state are explained mainlyby morphological changes, modification of its physiological andbiochemical properties that affect the refractive index and allowingthem to be differentiated from each other. The poles location andcoefficients will be affected accordingly. Therefore, if 85% of the FOMvalues result to be located in the right hand of the Z-plan—then thecell lines under study is considered to be cancer, else it is normal.There is a clear discrimination strategy—by performing opticalmeasurements on the different in vitro cell normal and cancer cell linemodels, the developed data processing procedure based on the Pronymethod works to achieve a label-free discrimination between cancer andhealthy cells from the same tissue type.

The present invention addresses the classification and discriminationbetween normal and cancer cells from the same tissues. A label-freemethod combining the Prony estimation theory and optical transmittancemeasurements is introduced and proven to be a powerful technique. Theproposed approach has been examined using four types of different celllines. The measured optical responses are reconstructed using the Pronyalgorithm with same fitting order of 40. Based on the observations, anormalized figure of merit (FOM) is introduced for identification. Basedon this merit, the distribution of the poles around the center of theunit circle of the normal cell lines is closer than the cancer celllines from same tissues (in the case of lung and liver cells). Thesefindings are considered the foundation stage for cell identificationusing optical measurements combined with the Prony estimation theory.

To plot the FOM, the following MATLAB code is developed. The functionhas two inputs, “a” and “b”. “a” represents the computed figure of meritfor the cancer cells, whereas “b” represents the computed figure ofmerit for the normal cells. The plot includes the unit circle forreference.

function [ ], graphpronyayesha(a, b)

figure % for opening new figure to plot the FOMs data.

[hz1, hp1]=zplane(a,a), % plot the input “a” as a cross in the z plane.

grid on,% turn on grids

hold on,% Plot the second FOM (for the normal cells) graph in samefigure

[hz2, hp2]=zplane(b,b), % plot the input “a” as a cross in the z plane.

hold off,

set(findobj(hz1, ‘Type’, ‘line’), ‘Color’, ‘b’), % color the input “a”in blue.

set(findobj(hp1, ‘Type’, ‘line’), ‘Color’, ‘b’), % color the input “a”in blue.

set(findobj(hz2, ‘Type’, ‘line’), ‘Color’, ‘r’), % color the input “b”in red.

set(findobj(hp2, ‘Type’, ‘line’), ‘Color’, ‘r’), % color the input “b”in red.

In another embodiment of the present invention, Autoregressive (AR)modeling techniques are used to fit a measured optical transmittance ofboth cancer and normal cells profiles. Analysis of variance (ANOVA)statistical approach is incorporated to determine the significant ARcoefficients. The transmitted light intensity passes through the cellsget affected by their intercellular compositions and membraneproperties. In this embodiment—four types of cells lung-cancer andnormal, liver-cancer and normal cells are suspended in theircorresponding media and their transmission characteristics are collectedand processed. The AR coefficients of each type of cell are analyzedwith the statistical technique ANOVA, which provided the significantcoefficients. The poles extracted from the significant coefficientsprovide an improved demarcation for normal and cancer cells. Theseoutcomes can be further utilized for cell classification usingstatistical tools.

The four type of cells utilized in this embodiment of the presentinvention, is shown in Table 2. The cell lines are processed accordingto the standards established by the American Tissue Culture Collection(ATCC). The cells from two different cell lines (lung—normal and cancerand liver—normal and cancer) used in this embodiment are cultured withthe corresponding culture medium.

TABLE 2 CELL LINE TISSUE CELL TYPE 1 BEAS 2B Lung Normal 2 CC-827 LungCancerous 3 THLE2 Liver Normal 4 HEP G2 Liver Cancerous

FIG. 7 shows an optical measurement setup or experimental setup inaccordance with the present invention, for measuring light transmissionintensity. A laptop 702 equipped with HMSEvaluation software is used formeasurements acquisition, an evaluation board (C113451-01) 704 is usedfor interface purpose. Other components included in the setup are anImage sensor (C11708 MA) 706, a sensor board (C113451-02), a sampleholder 710, a Convex lens holder 712 and a Xenon light source 714.C11708 MA mini-spectrometer optical sensor 706 is placed under a hostsample holder 710. High precision cell made of quartz superasil withlight path of 1 mm (Hellma analytics/Germany) is used as the host sampleholder. Each sample suspension is loaded inside the cell individually.The sensor board (C11351-02) output terminals are connected to theevaluation board (C113451-01) 704 to transfer data to a laptop 702. Thecollection of the light data and their corresponding processing arecarried out through the Hamamatsu-Mini-spectrometer for MS‘HMSEvaluation’ software installed on the laptop 702. A light xenonsource (Xenoncorp/USA) 714 is used to direct the light towards thesample. The sensor 706 converts the photo-electrical light that passesthrough the sample and convex lens 712 to electrical signal.

Considering the principles of the AR model in accordance with thepresent invention, a set of N discrete data samples are expressed in theAR (p) model as given in equation (6):

$\begin{matrix}{{x(n)} = {1 + {\sum\limits_{k = 1}^{p}{a_{k}{x\left( {n - k} \right)}}} + {e(n)}}} & (6)\end{matrix}$a(0)=1, n=1, 2, 3 . . . , N where x(n) is the present output, prepresents the order of the model, and a_(k) represents the ARcoefficients. e(n) represents the random shock or random noise and isassumed to be white Gaussian noise: WN(0, σ²). The all-pole model can berepresented in the z domain as follows:A(z)=(1+a ₁ z ⁻¹ +a ₂ z ⁻² + . . . +a _(p) z ^(−p))⁻¹  (7)

A key feature of AR modeling is that it does not have any domainconstraints. Any discrete signal, whether in time or frequency domain,can be modeled using an AR model. In addition, the signal may or may nothave transients. Metrics such as prediction accuracy, mean square error(MSE), and final prediction error (FPE) are helpful in evaluating theperformance of the model. The discrete dataset was preprocessed toremove noise and trend in the data. This was carried out by datasmoothing followed by data detrending.

The Final Prediction Error (FPE) is utilized for the estimation of themodel-fitting error and the use of the developed model to predict newoutputs. The selected model should minimize the FPE, which indeedrepresents a balance between the number of parameters and thevariations. The FPE measures the fit to estimated data, i.e. the modelquality. The Final Prediction Error (FPE) is defined by the followingequation:

$\begin{matrix}{{FPE} = {\left( \frac{1 + \left( {d/N} \right)}{1 - \left( {d/N} \right)} \right){SSE}}} & (8)\end{matrix}$where SSE is the sum of square error, N is the number of values in theestimation data set and d is the number of estimated parameters. Themean-square error (MSE) measures how closely the predictors tracks theactual data. The MSE is frequently used in the analysis of variance, andis calculated as follows:MSE=(x _(ia) −{circumflex over (x)} ₁)²  (9)where x_(ia) is the actual data point and {circumflex over (x)}₁, is theaverage of the actual data set. The squaring is used usually toexaggerate the influence of outliers.The SSE is then can be expressed as:

$\begin{matrix}{{SSE} = {\sum\limits_{i = 1}^{N}\left( {x_{ia} - \hat{x_{1}}} \right)^{2}}} & (10)\end{matrix}$

The F-statistic in ANOVA analysis that determine the significant ofcoefficients, mathematically can be expressed as follows:F=MSA/MSE  (11)where: MSA and MSE are the calculated mean of sum of all treatmentsquared errors and calculated mean of sum of squared errors,respectively. MSA and MSE are expressed as per follows:MSA=SSA/(k−1)  (12)MSE=SSE/(N−k)  (13)where k total number of samples (treatments) being considered and N isthe total number of observations (data points) available for alltreatments. SSA and SSE are sum of squared errors of all treatment(sample) means vs grand mean and sum of squared errors of allobservations vs respective sample means, respectively. The normal andcancer cells from the two cell lines (normal lung, cancerous lung,normal liver, and cancerous liver) are cultured separately. The norms ofthe ATCC are followed for the sub-culturing and trypsinization of thecells. The cell suspension preparation and culturing is done separatelyfor each cell type. The suspension for each cell type contained 107cells per mL. The cell count in the suspension of each type of cell isconducted using a hemocytometer with a 5% mean error. The suspension isthen loaded in the experimental setup and the optical profile of thecells in the suspension is recorded. The measured transmission profileof the benign and malignant cells of the lung and liver tissues areshown in FIG. 8A to D respectively (which depicts measured opticaltransmittance response of a (a) cancerous liver, (b) normal liver, (c)cancerous lung, and (d) normal lung cells. FIG. 8A shows measuredoptical transmittance response of a cancerous liver, FIG. 8B showsmeasured optical transmittance response of a normal liver, FIG. 8C showsmeasured optical transmittance response of a cancerous lung and FIG. 8Dshows measured optical transmittance response of normal lung cells inaccordance with the present invention. The measured transmittance issampled at a uniform sampling interval of Ws=2.3 nm).

TABLE 3 Prediction Type of cell accuracy MSE FPE Normal lung 91.61%5.72e−08 6.00e−08 Cancerous lung 90.75% 1.13e−07 1.18e−07 Normal liver93.48% 6.53e−08 6.84e−08 Cancerous liver 99.76% 6.59e−08 6.91e−08

Table 3 summarizes the metrics such as prediction accuracy, MSE, and FPEof the fitted AR model of each type of cell for an order 6. Thecomplexity of the model increases with the order of the model. The ARmodel coefficients of order 6 obtained for the four types of cells areshown in Table 4. It is concluded that the coefficients are of differentvalues for different types of cells. This reflects the alteration in thecomposition and intrinsic properties of the different cell types.Moreover, the normal and cancer cells from the same tissue havedifferent coefficient values, implying the variation in theircomposition, morphology, and intrinsic properties.

TABLE 4 AR Normal Cancerous Normal Cancerous Coefficients Lung LungLiver Liver a₁ −0.16 +0.06 −0.13 −0.68 a₂ −0.90 −0.94 −0.96 −0.95 a₃+0.03 −0.23 −0.08 +0.43 a₄ +0.42 +0.36 +0.46 +0.36 a₅ +0.05 +0.12 +0.13−0.09 a₆ +0.01 +0.02 +0.06 −0.01

Table 5 shows the poles extracted for the normal and cancer cells of thelung and liver tissue used in this work. The poles can be real valued orcomplex conjugate pairs. For instance, the poles P₁ and P₂ of livercancer cells are real and distinct, while their remaining poles (P₃-P₆)occur as complex conjugate pairs. All the extracted poles of lung(normal and cancer) cells occur as complex conjugate pairs.

TABLE 5 Cell Type P₁, P₂ P₃, P₄ P₅, P₆ Normal lung −0.687 ± 0.305i −0.05± 0.16i 0.820 ± 0.32i Cancerous lung −0.688 ± 0.309i −0.15 ± 0.21i 0.800± 0.24i Normal liver −0.71 ± 0.33i −0.10 ± 0.32i  0.88 ± 0.32i Cancerousliver −0.1 + 0i, 0.35 + 0i −0.65 ± 0.23i  0.87 ± 0.11i

The distribution of the poles of the normal and cancer cells of the lungand liver in the z-plane is shown in FIGS. 9A and 9B, respectively. Thepoles of the normal cells are illustrated with blue dots whereas the reddots represent the poles of the cancer cells. As shown in FIG. 9, thepoles of the different cells have different distributions in thez-plane. In addition, the distribution of the poles of the normal andcancer cells of the same tissue are also different. Any deviation fromthe pole values of the normal cells shows the presence of abnormalities.

The modifications of the optical response from normal to cancer stateare explained mainly by morphological changes, modification of itsphysiological and biochemical properties that affect the refractiveindex and allowing them to be differentiated from each other. The poleslocation and coefficients are affected accordingly. The applied opticalmeasurements conditions are not harsh to the cells. The applied lightdid not alter temperature of the cells. The O2 is dissolved in the mediawhich helps the cells to survive. The pH is maintained—and not affectedby light. The pH is measured before and after. The temperature of thesuspension is also measured before and after, and the measurements areconducted at room temperature. The cells are suspended in media that isrich with nutrient, to keep them alive. Cells are subjected to light forless than 5 minutes (which is not significant time for the cells to die,mainly the cells during measurements suspended inside the media). Cellviability test is used to check the suspension before and after theoptical measurements. Before the light percentage of living cell isabove 90%, and after the optical measurements—the percentage of livingcell is above 85%.

In an embodiment of the present invention, for reducing redundancy andto arrive at a concise AR model, statistical tools such as the N-wayANOVA technique are applied. The ANOVA revealed the significance of theAR coefficients. The ANOVA technique is applied to the AR coefficientsrather than the poles since the poles were extracted from thecoefficients. The coefficient that gives the highest value of the meansquare is the significant AR coefficient. Three coefficients—a1, a3, anda5—out of the six coefficients shown in Table 4 are found to besignificant. Hence the order of the AR model is reduced by one degree.This reduces the complexity of the system. A new set of reduced poledistribution in the Z-plan are plotted in FIG. 10. FIG. 10A shows theZ-plane showing distribution of the reduced poles of normal andcancerous lung cells and FIG. 10B shows the Z-plane showing distributionof normal and cancerous liver cells, in accordance with the presentinvention. The complex poles are defined as σ±jω, where σ is the dampingcoefficient (real part of the pole) and ω is the resonant pulsation(imaginary part of the pole). The poles damping and resonant is used toidentify the quality factor of the pole, as follows:Q=ω/2σ  (14)

In an embodiment of the present invention, the pole quality (Q) is thenused to discriminate between cancer and normal cells. The pole qualityis considered as a figure of merit (FOM), to correlate the real partwith the imaginary part to develop a discrimination procedure. Thelocation of the poles are strongly affected by differences in cellcomposition for normal and cancerous cells. Modifications of the opticalresponse from normal to cancer state are explained mainly bymorphological changes, modification of its physiological and biochemicalproperties that affect the refractive index and allowing them to bedifferentiated from each other; as previously indicated. The polesquality factors are affected accordingly. Empirically, the cancer cellsexhibit higher transmittance intensity when compared to normal ones fromthe same tissue type.

The corresponding computed poles quality factors for the four type ofcells under study are shown in FIG. 11A depicts the Z-plane showingdistribution of the Q-factor of normal and cancerous lung cells and FIG.11B depicts the Z-plane showing distribution of the Q-factor of normaland cancerous liver cells, in accordance with the present invention.FIG. 11 reveals that the magnitude of the pole quality factor for cancercells is higher than normal. Therefore, the proposed approach offers aclear discrimination strategy—by performing optical measurements on thedifferent in vitro cell normal and cancer cell line models, wherein thedeveloped data processing procedure based on the AR method to achieve alabel-free discrimination between cancer and healthy cells from the sametissue type works very well. Furthermore, the proposed approach may becoupled or integrated with existing techniques and methods to enhancethe discrimination between cancer and normal cells from same tissues.The current method utilizes statistical methods that are less expensivethan machine learning based methods.

Many changes, modifications, variations and other uses and applicationsof the subject invention will become apparent to those skilled in theart after considering this specification and the accompanying drawings,which disclose the preferred embodiments thereof. All such changes,modifications, variations and other uses and applications, which do notdepart from the spirit and scope of the invention, are deemed to becovered by the invention, which is to be limited only by the claimswhich follow.

The invention claimed is:
 1. A label-free method of detection of cancerous cells from an organ tissue, the method comprising the steps of: extracting a plurality of cells from the organ tissue; observing and measuring optical transmission characteristics of the plurality of cells using a light measuring device; modeling the measured optical transmission characteristics using a statistical algorithm; extracting a plurality of data parameters from the modelled optical transmission characteristics and further processing the extracted data parameters to obtain pole coefficients and locations corresponding to the plurality of cells; calculating a figure of merit (FOM) for the plurality of cells to classify cancerous cells, wherein the FOM is calculated from the pole coefficients and locations corresponding to the plurality of cells, and wherein modeling of the measured optical transmission characteristics comprises fitting the measured optical transmission characteristics with a sum of decaying exponential signals using Prony algorithm.
 2. The method of claim 1, wherein the plurality of cells extracted from the organ tissue are suspended in a cell culture medium.
 3. The method of claim 1, wherein the measured optical transmission characteristics comprise reflection, absorption and variable attenuation parameters.
 4. The method of claim 1, wherein the measured optical transmission characteristics are reconstructed using the Prony algorithm with a predefined fitting order.
 5. The method of claim 1, wherein variations in optical transmission characteristics of the plurality of cells are observed, due to differences in cell composition and intrinsic characteristics between normal and cancerous cells.
 6. The method of claim 1, wherein different coefficients and locations are observed for the plurality of cells, due to differences in cell composition and intrinsic characteristics between normal and cancerous cells.
 7. The method of claim 1, wherein the FOM is defined as FOM(p))=(p), where C(p) and L(p) represent the pole coefficients and locations respectively, for the suspended plurality of cells.
 8. The method of claim 1, wherein pole quality (Q) is used as a figure of merit (FOM).
 9. A label-free method of detection of cancerous cells from an organ tissue, the method comprising the steps of: extracting a plurality of cells from the organ tissue; observing and measuring optical transmission characteristics of the plurality of cells using a light measuring device; modeling the measured optical transmission characteristics using a statistical algorithm; extracting a plurality of data parameters from the modelled optical transmission characteristics and further processing the extracted data parameters to obtain pole coefficients and locations corresponding to the plurality of cells; and calculating a figure of merit (FOM) for the plurality of cells to classify cancerous cells, wherein the FOM is calculated from the pole coefficients and locations corresponding to the plurality of cells, wherein an Autoregressive (AR) modeling technique is used to model the measured optical transmission characteristics of the plurality of cells.
 10. The method of claim 9, wherein analysis of variance (ANOVA) statistical approach is incorporated for determining significant AR coefficients, and poles are extracted from the determined significant AR coefficients, to provide a demarcation for cancerous cells. 